spss-modeler

问题 I am new to SPSS modeler. I am triyng to create a simple data transformation with python on a dummy created data. The dummy data is created as exected. (see at the bottom) I try to access and modify the data with python using the example that i found on IBM website import spss.pyspark.runtime from pyspark.sql.types import * cxt = spss.pyspark.runtime.getContext() if cxt.isComputeDataModelOnly(): _schema = cxt.getSparkInputSchema() cxt.setSparkOutputSchema(_schema) else: _structType = cxt

问题 I have a question regarding apriori rule deployment in R. I basically want to assign a predcition(item) and a confidence value to each customer so I can create a simple recommending system, so below is a subset of my rule set which I have obtained, bread&wine -> meat (confidence 54%) cheese -> fruit (confidence 43%) bread&cheese -> frozveg (confidence 24%) and the following is simple representation of what I want to achieve with just 1 customer; this is in a basket or truth-table data. ID

问题 I'm very new to SPSS Modeler and previously used SAS Guide & Miner to schedule batches and scheduled runs. We currently manually run streams weekly then export them into Excel to use for reporting. Is there any way to schedule these streams to run automatically? Thank you. 回答1: ModelerBatch can be used only if you have the SPSS Modeler Server installed. I use the task scheduler from Windows but first you have to write an archive .bat Example of .bat: call "C:\Program Files\IBM\SPSS\Modeler\18